Introduces a hybrid pipeline combining random-forest feature selection with fuzzy c-means clustering and selective manual review to classify MER signals into zona incerta, STN, and substantia nigra with ~92.7% accuracy while reducing manual labeling to ~10%.

By enabling fast, scalable, and accurate intraoperative identification of DBS target nuclei, this method can improve surgical targeting and efficiency—potentially enhancing clinical outcomes and facilitating wider deployment of DBS—though it does not directly inform molecular mechanisms or novel…

In deep brain stimulation (DBS) surgery for Parkinson's disease (PD), the accurate intraoperative identification of key nuclei-such as the subthalamic nucleus (STN)-is critical to ensuring therapeutic efficacy. However, current approaches heavily depend on expert annotations, and classification tasks are typically confined to binary distinctions between STN and non-STN regions. This limitation hampers the real-time recognition of complex and diverse neuroanatomical structures during PD-related DBS procedures. To overcome this challenge, we propose a three-class classification strategy for microelectrode recording (MER) signals to effectively distinguish the zona incerta (Zi), STN, and substantia nigra (SN). This approach integrates supervised feature selection with unsupervised clustering: discriminative features are first selected using the random forest algorithm; these features are then input into fuzzy c-means (FCM) clustering for preliminary classification; finally, samples with low-confidence scores are manually reviewed. This strategy forms an efficient and verifiable label-generation mechanism that improves classification accuracy and enhances clinical applicability. Experimental results show that the proposed "clustering + review" labeling framework achieves an overall classification accuracy of 92.71%, with only about 10% of samples requiring manual verification-closely matching the 92.97% accuracy achieved with expert labeling and significantly improving labeling efficiency. Furthermore, the ROC-AUC values for all three nuclei (Zi, STN, and SN) exceed 0.97, confirming the model's robust discriminative performance. By combining supervised and unsupervised techniques, the proposed multinuclei classification framework for MER signals not only ensures high accuracy while substantially reducing manual annotation costs but also offers a scalable and efficient solution for rapid neural signal labeling. This method is particularly well-suited for real-time applications such as intraoperative target localization during DBS and shows strong potential for clinical translation.